Comparison of artificial neural network and multivariate regression models for prediction of Azotobacteria population in soil under different land uses
Authors:
- Mitra EbrahimiMEMitra Ebrahimi
- Ali Akbar Safari SineganiASAli Akbar Safari Sinegani
- Mohammad Reza SarikhaniMSMohammad Reza Sarikhani
- Seyed Abolghasem MohammadiSMSeyed Abolghasem Mohammadi
- 1 August 2017
- journal article
- Published by Elsevier in Computers and Electronics in Agriculture
- Vol. 140, 409-421
Abstract
No abstract availableFunding Information
- Iran National Science Foundation – Iran
- University of Tabriz – Iran
- Bu-Ali Sina University of Hamedan – Iran
This publication has 33 references indexed in Scilit:
- Artificial neural network model for estimating the soil temperatureCanadian Journal of Soil Science, 2011
- On the relevance of using artificial neural networks for estimating soil moisture contentJournal of Hydrology, 2008
- Modeling hydraulic properties of sandy soils of Niger using pedotransfer functionsGeoderma, 2007
- Classifying regenerating forest stages in Amazônia using remotely sensed images and a neural networkForest Ecology and Management, 2006
- Biodegradability of soluble organic matter in maize-cropped soilsGeoderma, 2003
- Development and use of a database of hydraulic properties of European soilsGeoderma, 1999
- Progress in the biochemistry of nitrogen fixationProceedings of the Royal Society of London. B. Biological Sciences, 1969
- Studies on nitrogen-fixing bacteria of the genusBeijerinckiaPlant and Soil, 1961
- A RAPID MANOMETEIC METHOD FOR DETERMINING SOIL CARBONATESoil Science, 1955
- A CRITICAL EXAMINATION OF A RAPID METHOD FOR DETERMINING ORGANIC CARBON IN SOILS—EFFECT OF VARIATIONS IN DIGESTION CONDITIONS AND OF INORGANIC SOIL CONSTITUENTSSoil Science, 1947